June 2023 arXiv papers

Pierre Nzabahimana, Pawel Danielewicz

In heavy-ion collisions, low relative-velocity two-particle correlations have been a tool for assessing space-time characteristics of particle emission. Those characteristics may be cast in the form of a relative emission source related to the correlation function through the Koonin-Pratt (KP) convolution formula that involves the relative wave-function for

Eric J. Sung, Andre G. Campos, Hartmut Abele, Denys I. Bondar

The theory of entropic gravity conjectures that gravity emerges thermodynamically rather than being a fundamental force. One of the main criticisms of entropic gravity is that it would lead to quantum massive particles losing coherence in free fall, which is not observed experimentally. This criticism was refuted in [Phys. Rev. Res. 3, 033065 (2021)], where

Yun Chen, Nuria González-Prelcic, Takayuki Shimizu, Hongsheng Lu

One strategy to obtain user location information in a wireless network operating at millimeter wave (mmWave) is based on the exploitation of the geometric relationships between the channel parameters and the user position. These relationships can be built from the line-of-sight (LOS) path and first-order reflections, or purely first-order reflections, requir

Marco Clementi, Edgars Nitiss, Elena Durán-Valdeiglesias, Sofiane Belahsene

Second-harmonic generation allows for coherently bridging distant regions of the optical spectrum, with applications ranging from laser technology to self-referencing of frequency combs. However, accessing the nonlinear response of a medium typically requires high-power bulk sources, specific nonlinear crystals, and complex optical setups, hindering the path

Ankita Pasad, Chung-Ming Chien, Shane Settle, Karen Livescu

Many self-supervised speech models (S3Ms) have been introduced over the last few years, improving performance and data efficiency on various speech tasks. However, these empirical successes alone do not give a complete picture of what is learned during pre-training. Recent work has begun analyzing how S3Ms encode certain properties, such as phonetic and spea

Satya Prakash Pradhan, Arash Yavari

In this paper we formulate a geometric nonlinear theory of the mechanics of accreting-ablating bodies. This is a generalization of the theory of accretion mechanics of Sozio and Yavari (2019). More specifically, we are interested in large deformation analysis of bodies that undergo a continuous and simultaneous accretion and ablation on their boundaries whil

Zizheng Pan, Jing Liu, Haoyu He, Jianfei Cai

Large pretrained plain vision Transformers (ViTs) have been the workhorse for many downstream tasks. However, existing works utilizing off-the-shelf ViTs are inefficient in terms of training and deployment, because adopting ViTs with individual sizes requires separate trainings and is restricted by fixed performance-efficiency trade-offs. In this paper, we a

Josh Pollock, Catherine Mei, Grace Huang, Elliot Evans

Diagrams are essential tools for problem-solving and communication as they externalize conceptual structures using spatial relationships. But when picking a diagramming framework, users are faced with a dilemma. They can either use a highly expressive but low-level toolkit, whose API does not match their domain-specific concepts, or select a high-level typol

Sibylle Marcotte, Rémi Gribonval, Gabriel Peyré

Understanding the geometric properties of gradient descent dynamics is a key ingredient in deciphering the recent success of very large machine learning models. A striking observation is that trained over-parameterized models retain some properties of the optimization initialization. This "implicit bias" is believed to be responsible for some favorab

Hari Venugopalan, Kaustav Goswami, Zainul Abi Din, Jason Lowe-Power

Device fingerprinting leverages attributes that capture heterogeneity in hardware and software configurations to extract unique and stable fingerprints. Fingerprinting countermeasures attempt to either present a uniform fingerprint across different devices through normalization or present different fingerprints for the same device each time through obfuscati

Patrick Emami, Abhijeet Sahu, Peter Graf

Short-term forecasting of residential and commercial building energy consumption is widely used in power systems and continues to grow in importance. Data-driven short-term load forecasting (STLF), although promising, has suffered from a lack of open, large-scale datasets with high building diversity. This has hindered exploring the pretrain-then-fine-tune p

Giuseppe Alessio D'Inverno, Simone Brugiapaglia, Mirco Ravanelli

Graph Neural Networks (GNNs) have emerged as a powerful tool for data-driven learning on various graph domains. They are usually based on a message-passing mechanism and have gained increasing popularity for their intuitive formulation, which is closely linked to the Weisfeiler-Lehman (WL) test for graph isomorphism to which they have been proven equivalent

Jan Trieschmann, Luca Vialetto, Tobias Gergs

Machine learning has had an enormous impact in many scientific disciplines. Also in the field of low-temperature plasma modeling and simulation it has attracted significant interest within the past years. Whereas its application should be carefully assessed in general, many aspects of plasma modeling and simulation have benefited substantially from recent de

Reza Mohammadi, Marit Schoonhoven, Lucas Vogels, S. Ilker Birbil

Bayesian methods for learning Gaussian graphical models offer a principled framework for quantifying model uncertainty and incorporating prior knowledge. However, their scalability is constrained by the computational cost of jointly exploring graph structures and precision matrices. To address this challenge, we perform inference directly on the graph by int

Eleftherios Triantafyllidis, Fernando Acero, Zhaocheng Liu, Zhibin Li

Solving long sequential tasks poses a significant challenge in embodied artificial intelligence. Enabling a robotic system to perform diverse sequential tasks with a broad range of manipulation skills is an active area of research. In this work, we present a Hybrid Hierarchical Learning framework, the Robotic Manipulation Network (ROMAN), to address the chal

Thomas Bitoun

Let Z be the germ of a complex hypersurface isolated singularity of equation f, with Z at least of dimension 2. We consider the family of analytic D-modules generated by the powers of 1/f and describe it in terms of the pole order filtration on the de Rham cohomology of the complement of {f=0} in the neighborhood of the singularity.

Vivek Myers, Andre He, Kuan Fang, Homer Walke

Our goal is for robots to follow natural language instructions like "put the towel next to the microwave." But getting large amounts of labeled data, i.e. data that contains demonstrations of tasks labeled with the language instruction, is prohibitive. In contrast, obtaining policies that respond to image goals is much easier, because any autonomous

Vladimir Kolmogorov

Currently, the best known tradeoff between approximation ratio and complexity for the Sparsest Cut problem is achieved by the algorithm in [Sherman, FOCS 2009]: it computes $O(\sqrt{(\log n)/\varepsilon})$-approximation using $O(n^\varepsilon\log^{O(1)}n)$ maxflows for any $\varepsilon\in[Θ(1/\log n),Θ(1)]$. It works by solving the SDP relaxation of [Arora-R

Jilang Miao, Miaomiao Jin

This paper presents an accurate S$_N$ solver for slab geometry. For constant cross-section regions, it gives accurate angular fluxes without need of fine meshes or approximation of solution forms.

Prabin Sharma, Kisan Thapa, Dikshya Thapa, Prastab Dhakal

Artificial intelligence is gaining traction in more ways than ever before. The popularity of language models and AI-based businesses has soared since ChatGPT was made available to the general public via OpenAI. It is becoming increasingly common for people to use ChatGPT both professionally and personally. Considering the widespread use of ChatGPT and the re

David C. Garrett, Jinhua Xu, Yousuf Aborahama, Geng Ku

Ultrasonography is a vital component of modern clinical care, with handheld probes routinely used for diagnostic imaging and procedural guidance. However, handheld ultrasound imaging is limited by factors such as the partial-cross-sectional field of view, operator dependency, contact-induced distortion, and lack of transmission contrast. Here, we demonstrate

Grigor Sargsyan

The goal of this paper is to present an approach to Hod Pair Capturing (HPC). $HPC$ is the most outstanding open problem of descriptive inner model theory. More specifically, we introduce two principles, the Direct Limit Independence and the Bounded Direct Limits, and show that they together imply HPC.

Eduardo V. L. Barboza, Paulo R. Lisboa de Almeida, Alceu de Souza Britto, Rafael M. O. Cruz

Data normalization is an essential task when modeling a classification system. When dealing with data streams, data normalization becomes especially challenging since we may not know in advance the properties of the features, such as their minimum/maximum values, and these properties may change over time. We compare the accuracies generated by eight well-kno

Hooman Davoudiasl, Roman Marcarelli, Ethan T. Neil

The Electron-Ion Collider (EIC) provides unique opportunities in searching for new physics through its high center of mass energy and coherent interactions of large nuclei. We examine how light weakly interacting vector bosons from a variety of models can be discovered or constrained, over significant parts of their parameter space, through clean displaced v

Ian L. V. Roque, Nima Razavi-Ghods, Steven H. Carey, John A. Ely

We detail the the REACH radiometric system designed to enable measurements of the 21-cm neutral hydrogen line. Included is the radiometer architecture and end-to-end system simulations as well as a discussion of the challenges intrinsic to highly-calibratable system development. Following this, we share laboratory results based on the calculation of noise wa

Balamurali Murugesan, Rukhshanda Hussain, Rajarshi Bhattacharya, Ismail Ben Ayed

Recently, CLIP-based approaches have exhibited remarkable performance on generalization and few-shot learning tasks, fueled by the power of contrastive language-vision pre-training. In particular, prompt tuning has emerged as an effective strategy to adapt the pre-trained language-vision models to downstream tasks by employing task-related textual tokens. Mo

Anton Bernshteyn, Abhishek Dhawan

We show that every Borel graph $G$ of subexponential growth has a Borel proper edge-coloring with $Δ(G) + 1$ colors. We deduce this from a stronger result, namely that an $n$-vertex (finite) graph $G$ of subexponential growth can be properly edge-colored using $Δ(G) + 1$ colors by an $O(\log^\ast n)$-round deterministic distributed algorithm in the $\mathsf{

Søren Taverniers, Svyatoslav Korneev, Christoforos Somarakis, Morad Behandish

The computation of damping rates of an oscillating fluid with a free surface in which viscosity is small and surface tension high is numerically challenging. A typical application requiring such computation is drop-on-demand (DoD) microfluidic devices that eject liquid metal droplets, where accurate knowledge of damping rates for the least-damped oscillation

Melody Huang, Dan Soriano, Samuel D. Pimentel

Sensitivity to unmeasured confounding is not typically a primary consideration in designing treated-control comparisons in observational studies. We introduce a framework allowing researchers to optimize robustness to omitted variable bias at the design stage using a measure called design sensitivity. Design sensitivity, which describes the asymptotic power

Yeqin Liu

We show that when a K3 surface acquires a node, the existence of stable spherical sheaves of certain Chern classes can be obstructed.

Alireza F. Behbahani, Vagelis Harmandaris

We analyze the displacements of the particles of a glass-forming molecular liquid perpendicular to a confining solid surface, using extensive molecular dynamics simulations with atomistic models. In the vicinity of an attractive surface, the liquid molecules are trapped. Transient localization of liquid molecules near the surface introduces a relaxation proc

Lianxin Zhang, Yihan Huang, Zhongzhong Cao, Yang Jiao

Parallel self-assembly is an efficient approach to accelerate the assembly process for modular robots. However, these approaches cannot accommodate complicated environments with obstacles, which restricts their applications. This paper considers the surrounding stationary obstacles and proposes a parallel self-assembly planning algorithm named SAPOA. With th

Milind N. Kunchur

This work reviews the human auditory system, elucidating some of the specialized mechanisms and non-linear pathways along the chain of events between physical sound and its perception. Customary relationships between frequency, time, and phase--such as the uncertainty principle--that hold for linear systems, do not apply straightforwardly to the hearing proc

Axel Lazzarotto, Alain Hui-Bon-Hoa, Michel Rieutord

Intermediate-mass stars are often fast rotators, and hence are centrifugally flattened and affected by gravity darkening. To analyse this kind of stars properly, one must turn to 2D models to compute the visible radiative flux and to take the geometrical effect of the star inclination into account. Assuming a given stellar age and chemical composition, we ai

Maciej Pieczarka, Marcin Gębski, Aleksandra N. Piasecka, James A. Lott

Many bosons can occupy a single quantum state without a limit. This state is described by quantum-mechanical Bose-Einstein statistics, which allows the formation of a Bose-Einstein condensate at low temperatures and high particle densities. Photons, historically the first considered bosonic gas, were late to show this phenomenon, which was observed in rhodam

Peter Mayr, Patrick Wynne

Clonoids are sets of finitary functions from an algebra $\mathbb{A}$ to an algebra $\mathbb{B}$ that are closed under composition with term functions of $\mathbb{A}$ on the domain side and with term functions of $\mathbb{B}$ on the codomain side. For $\mathbb{A},\mathbb{B}$ (polynomially equivalent to) finite modules we show: If $\mathbb{A},\mathbb{B}$ have

Yifan He, Svetlozar Rachev

In this paper, we mainly study the impact of the implied certainty equivalent rate on investment in financial markets. First, we derived the mathematical expression of the implied certainty equivalent rate by using put-call parity, and then we selected some company stocks and options; we considered the best-performing and worst-performing company stocks and

Felix Cherubini, Thierry Coquand, Matthias Hutzler

This is a foundation for algebraic geometry, developed internal to the Zariski topos, building on the work of Kock and Blechschmidt. The Zariski topos consists of sheaves on the site opposite to the category of finitely presented algebras over a fixed ring, with the Zariski topology, i.e. generating covers are given by localization maps $A\to A_{f_1}$ for fi

Kshitij Goel, Wennie Tabib

This paper introduces the open-source framework, GIRA, which implements fundamental robotics algorithms for reconstruction, pose estimation, and occupancy modeling using compact generative models. Compactness enables perception in the large by ensuring that the perceptual models can be communicated through low-bandwidth channels during large-scale mobile rob

Djuna Croon

These lectures, presented at the 2022 TASI summer school, give an introductory overview of first-order phase transitions in the early Universe, baryogenesis, and the resulting gravitational wave phenomenology. We introduce thermal field theory via the imaginary time formalism, and comment on the pitfalls of 1-loop calculations and alternative approaches. The

Martin Bojowald, David Brizuela, Paula Calizaya Cabrera, Sara F. Uria

A quantum analysis of the vacuum Bianchi IX model is performed, focusing in particular on the chaotic nature of the system. The framework constructed here is general enough for the results to apply in the context of any theory of quantum gravity, since it includes only minimal approximations that make it possible to encode the information of all quantum degr

Prashant Kumar, R. N. Bhatt

We study the scaling properties of the entanglement entropy (EE) near quantum critical points in interacting random antiferromagnetic (AF) spin chains. Using density-matrix renormalization group, we compute the half-chain EE near the topological phase transition between Haldane and Random Singlet phases in a disordered spin-1 chain. It is found to diverge lo

Matilde Barberi Squarotti, Stefano Camera, Roy Maartens

We apply the multi-tracer technique to test the possibility of improved constraints on the amplitude of local primordial non-Gaussianity, $f_{\mathrm{NL}}$, in the cosmic large-scale structure. A precise measurement of $f_{\mathrm{NL}}$ is difficult because the effects of non-Gaussianity mostly arise on the largest scales, which are heavily affected by the l

Valentin Benedetti, Lucas Daguerre

We study the universal logarithmic coefficient of the entanglement entropy (EE) in a sphere for free fermionic field theories in a $d=4$ Minkowski spacetime. As a warm-up, we revisit the free massless spin-$1/2$ field case by employing a dimensional reduction to the $d=2$ half-line and a subsequent numerical real-time computation on a lattice. Surprisingly,

Jason D. McEwen, Tobías I. Liaudat, Matthew A. Price, Xiaohao Cai

Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging. The framework is suitable for models with a log-convex likelihood, which are ubiquitous in the imaging sciences. The purpose of this article is two-fold. First, we review proximal nested sampling in a pedagogical m

Diego Buccio, John F. Donoghue, Roberto Percacci

We explore the properties of a simple renormalizable shift symmetric model with a higher derivative kinetic energy and quartic derivative coupling, that can serve as a toy model for higher derivative theories of gravity. The scattering amplitude behaves as in a normal effective field theory below the threshold for the production of ghosts, but has an unexpec

Konstantin Tiurev, Arthur Pesah, Peter-Jan H. S. Derks, Joschka Roffe

We introduce the domain wall color code, a new variant of the quantum error-correcting color code that exhibits exceptionally high code-capacity error thresholds for qubits subject to biased noise. In the infinite bias regime, a two-dimensional color code decouples into a series of repetition codes, resulting in an error-correcting threshold of 50%. Interest

Hayden R. Foote, Gurtina Besla, Philip Mocz, Nicolás Garavito-Camargo

The Large Magellanic Cloud (LMC) will induce a dynamical friction (DF) wake on infall to the Milky Way (MW). The MW's stellar halo will respond to the gravity of the LMC and the dark matter (DM) wake, forming a stellar counterpart to the DM wake. This provides a novel opportunity to constrain the properties of the DM particle. We present a suite of high-

Michael Y. Grudić, Stella S. R. Offner, Dávid Guszejnov, Claude-André Faucher-Giguère

When a detailed model of a stellar population is unavailable, it is most common to assume that stellar masses are independently and identically distributed according to some distribution: the universal initial mass function (IMF). However, stellar masses resulting from causal, long-ranged physics cannot be truly random and independent, and the IMF may vary w

Kristen Garofali, Antara R. Basu-Zych, Benjamin D. Johnson, Panayiotis Tzanavaris

We present a methodology for modeling the joint ionizing impact due to a "simple X-ray population" (SXP) and its corresponding simple stellar population (SSP), where "simple" refers to a single age and metallicity population. We construct composite spectral energy distributions (SEDs) including contributions from ultra-luminous X-ray sources

Caio Nascimento, Marilena Loverde

We develop a first-principles formalism to compute the distortion to the relic neutrino density field caused by the peculiar motions of large-scale structures. This distortion slows halos down due to dynamical friction, causes a local anisotropy in the neutrino-CDM cross-correlation, and reduces the global cross-correlation between neutrinos and CDM. The loc

Alicja Polanska, Matthew A. Price, Alessio Spurio Mancini, Jason D. McEwen

Computing the marginal likelihood (also called the Bayesian model evidence) is an important task in Bayesian model selection, providing a principled quantitative way to compare models. The learned harmonic mean estimator solves the exploding variance problem of the original harmonic mean estimation of the marginal likelihood. The learned harmonic mean estima

Berndt Müller, Xiaojun Yao

We find a simple spin Hamiltonian to describe physical states of $2+1$ dimensional SU(2) lattice gauge theory on a honeycomb lattice with a truncation of the electric field representation at $j_{\rm max}=\frac{1}{2}$. The simple spin Hamiltonian only contains local products of Pauli matrices, even though Gauss's law has been completely integrated out.

Andrew Spiers, Adam Pound, Barry Wardell

High-accuracy gravitational-wave modeling demands going beyond linear, first-order perturbation theory. Particularly motivated by the need for second-order perturbative models of extreme-mass-ratio inspirals and black hole ringdowns, we present practical spherical-harmonic decompositions of the Einstein equation, Regge-Wheeler-Zerilli equations, and Teukolsk

Raffael L. Klees, Daniel Gresta, Jonathan Sturm, Laurens W. Molenkamp

The unambiguous identification of Majorana zero modes (MZMs) is one of the most outstanding problems of condensed matter physics. Thermal transport provides a detection tool that is sensitive to these chargeless quasiparticles. We study thermoelectric transport between metallic leads transverse to a Josephson junction. The central double quantum dot hosts co

Ziqian Zhong, Ziming Liu, Max Tegmark, Jacob Andreas

Do neural networks, trained on well-understood algorithmic tasks, reliably rediscover known algorithms for solving those tasks? Several recent studies, on tasks ranging from group arithmetic to in-context linear regression, have suggested that the answer is yes. Using modular addition as a prototypical problem, we show that algorithm discovery in neural netw

Guocheng Qian, Jinjie Mai, Abdullah Hamdi, Jian Ren

We present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-r

Lijun Yu, Yong Cheng, Zhiruo Wang, Vivek Kumar

In this work, we introduce Semantic Pyramid AutoEncoder (SPAE) for enabling frozen LLMs to perform both understanding and generation tasks involving non-linguistic modalities such as images or videos. SPAE converts between raw pixels and interpretable lexical tokens (or words) extracted from the LLM's vocabulary. The resulting tokens capture both the sem

Yann Gouttenoire, Edoardo Vitagliano

Recently, Pulsar Timing Array (PTA) collaborations have detected a stochastic gravitational wave background (SGWB) at nano-Hz frequencies, with Domain Wall networks (DWs) proposed as potential sources. To be cosmologically viable, they must annihilate before dominating the universe energy budget, thus generating a SGWB. While sub-horizon DWs shrink and decay

Takuma Yoneda, Jiading Fang, Peng Li, Huanyu Zhang

There has been a significant research interest in employing large language models to empower intelligent robots with complex reasoning. Existing work focuses on harnessing their abilities to reason about the histories of their actions and observations. In this paper, we explore a new dimension in which large language models may benefit robotics planning. In

Moses Charikar, Prasanna Ramakrishnan, Kangning Wang, Hongxun Wu

We consider the following well-studied problem of metric distortion in social choice. Suppose we have an election with $n$ voters and $m$ candidates located in a shared metric space. We would like to design a voting rule that chooses a candidate whose average distance to the voters is small. However, instead of having direct access to the distances in the me

Joseph Tindall, Matthew T. Fishman

Effectively compressing and optimizing tensor networks requires reliable methods for fixing the latent degrees of freedom of the tensors, known as the gauge. Here we introduce a new algorithm for gauging tensor networks using belief propagation, a method that was originally formulated for performing statistical inference on graphical models and has recently

Paul Frederik Depta, Kai Schmidt-Hoberg, Pedro Schwaller, Carlo Tasillo

In this work we evaluate whether the gravitational wave background recently observed by a number of different pulsar timing arrays could be due to merging primordial supermassive black hole binaries. We find that for homogeneously distributed primordial black holes this possibility is inconsistent with strong cosmological and astrophysical constraints on the

Keisuke Inomata, Kazunori Kohri, Takahiro Terada

Multiple pulsar timing array (PTA) collaborations recently announced the evidence of common-spectral processes caused by gravitational waves (GWs). These can be the stochastic GW background and its origin may be astrophysical and/or cosmological. We interpret it as the GWs induced by the primordial curvature perturbations and discuss their implications on pr

Kavosh Asadi, Rasool Fakoor, Shoham Sabach

We focus on the task of approximating the optimal value function in deep reinforcement learning. This iterative process is comprised of solving a sequence of optimization problems where the loss function changes per iteration. The common approach to solving this sequence of problems is to employ modern variants of the stochastic gradient descent algorithm su

Michael J. Quin, Antonino Di Piazza, Christoph H. Keitel, Matteo Tamburini

The dynamics of relativistic particles in an intense electromagnetic field can be described by the Landau-Lifshitz (LL) equation, where the adiation reaction (RR) is accounted for via a self-force, and interparticle fields are often neglected as an approximation. However, the inclusion of interparticle fields is necessary to ensure energy-momentum conservati

Cindy Péralle, Sushanth Kini Manjeshwar, Anastasiia Ciers, Witlef Wieczorek

We present a detailed study of mechanically compliant, photonic-crystal-based microcavities featuring a quasi-bound state in the continuum. Such systems have recently been predicted to reduce the optical loss in Fabry-Perot-type optomechanical cavities. However, they require two identical photonic-crystal slabs facing each other, which poses a considerable c

Vinit Hegiste, Tatjana Legler, Martin Ruskowski

Federated learning (FL) has gained significant traction as a privacy-preserving algorithm, but the underlying resemblances of federated learning algorithms like Federated averaging (FedAvg) or Federated SGD (Fed SGD) to ensemble learning algorithms have not been fully explored. The purpose of this paper is to examine the application of FL to object detection

Yuanshun Yao, Yang Liu

Identifying the causes of a model's unfairness is an important yet relatively unexplored task. We look into this problem through the lens of training data - the major source of unfairness. We ask the following questions: How would the unfairness of a model change if its training samples (1) were collected from a different (e.g. demographic) group, (2) we

Siyu Jiang, Aidi Yang, Jiucheng Ma, Fa Peng Huang

For the first time, the expected stochastic gravitational wave background is probably discovered after observing the Hellings Downs correlation curve by several pulsar timing array (PTA) collaborations around the globe including NANOGrav, European PTA, Parkes PTA, and Chinese PTA. These new observations can help to explore or constrain the dark matter (DM) f

Bruno J. Barros, Álvaro de la Cruz-Dombriz, Francisco S. N. Lobo

In this work, we present novel analytical solutions for static and spherically symmetric wormhole geometries threaded by an anisotropic distribution of matter conformally coupled to a scalar ghost field. We explore the main features of the theory, such as the dynamics of the scalar field and matter throughout the wormhole, as well as the role played by the n

Sinan G. Aksoy, Ilya Amburg, Stephen J. Young

While multilinear algebra appears natural for studying the multiway interactions modeled by hypergraphs, tensor methods for general hypergraphs have been stymied by theoretical and practical barriers. A recently proposed adjacency tensor is applicable to nonuniform hypergraphs, but is prohibitively costly to form and analyze in practice. We develop tensor ti

Jeffrey Yelton

Let $K$ be a field with a discrete valuation, and let $p$ be a prime. It is known that if $Γ\lhd Γ_0 < \mathrm{PGL}_2(K)$ is a Schottky group normally contained in a larger group which is generated by order-$p$ elements each fixing $2$ points $a_i, b_i \in \mathbb{P}_K^1$, then the quotient of a certain subset of the projective line $\mathbb{P}_K^1$ by the a

Yi-Fu Cai, Xin-Chen He, Xiao-Han Ma, Sheng-Feng Yan

Recently, the NANOGrav, PPTA, EPTA, and CPTA collaborations independently reported their evidence of the Stochastic Gravitational Waves Background (SGWB). While the inferred gravitational-wave background amplitude and spectrum are consistent with astrophysical expectations for a signal from the population of supermassive black-hole binaries (SMBHBs), the sea

Tom Broadhurst, Chao Chen, Tao Liu, Kai-Feng Zheng

We explore orbital implications of the Supermassive Black Hole (SMBH) binary in UGC4211 for the energy spectrum of stochastic gravitational wave background (SGWB), measured with pulsar timing. The SMBH binary in UGC4211 has a projected separation of $\sim 230\,$pc and relative velocity of $\sim 150\,$km/s along the line of sight. It orbits with a disk of gas

Yiming Wang, Zhuosheng Zhang, Pei Zhang, Baosong Yang

Neural-symbolic methods have demonstrated efficiency in enhancing the reasoning abilities of large language models (LLMs). However, existing methods mainly rely on syntactically mapping natural languages to complete formal languages like Python and SQL. Those methods require that reasoning tasks be convertible into programs, which cater to the computer execu

Tan Wang, Linjie Li, Kevin Lin, Yuanhao Zhai

Generative AI has made significant strides in computer vision, particularly in text-driven image/video synthesis (T2I/T2V). Despite the notable advancements, it remains challenging in human-centric content synthesis such as realistic dance generation. Current methodologies, primarily tailored for human motion transfer, encounter difficulties when confronted

Marco Frasca, Anish Ghoshal

We study possible hints towards confinement in a Z$_2$-invariant Yukawa system with massless fermions and a real scalar field in the strongly-coupled regime. Using the tools developed for studying non-perturbative physics via Jacobi elliptical functions, for a given but not unique choice of the vacuum state, we find the exact Green&#39;s function for the sca

Theophile Gervet, Zhou Xian, Nikolaos Gkanatsios, Katerina Fragkiadaki

3D perceptual representations are well suited for robot manipulation as they easily encode occlusions and simplify spatial reasoning. Many manipulation tasks require high spatial precision in end-effector pose prediction, which typically demands high-resolution 3D feature grids that are computationally expensive to process. As a result, most manipulation pol

Yunchuan Zhang, Sangwoo Park, Osvaldo Simeone

Black-box zero-th order optimization is a central primitive for applications in fields as diverse as finance, physics, and engineering. In a common formulation of this problem, a designer sequentially attempts candidate solutions, receiving noisy feedback on the value of each attempt from the system. In this paper, we study scenarios in which feedback is als

Mathias Wanner, Igor Mezić

The Sparse Identification of Nonlinear Dynamics (SINDy) algorithm can be applied to stochastic differential equations to estimate the drift and the diffusion function using data from a realization of the SDE. The SINDy algorithm requires sample data from each of these functions, which is typically estimated numerically from the data of the state. We analyze

Kota Saito

A sequence of integers of the form $\lfloor n^α\rfloor$ $(n=1,2,\ldots)$ for some fixed non-integral $α>1$ is called a Piatetski-Shapiro sequence, where $\lfloor x\rfloor$ denotes the integer part of $x$. Let $\mathrm{PS}(α)$ denote the set of all those terms. In this article, we show that $x+y=z$ has only finitely many solutions $(x,y,z)\in \mathrm{PS}(α)^3

Emily Silcock, Melissa Dell

A diversity of tasks use language models trained on semantic similarity data. While there are a variety of datasets that capture semantic similarity, they are either constructed from modern web data or are relatively small datasets created in the past decade by human annotators. This study utilizes a novel source, newly digitized articles from off-copyright,

D. A. Long, J. R. Stroud, B. J. Reschovsky, Y. Bao

Cavity optomechanical sensors can offer exceptional sensitivity; however, interrogating the cavity motion with high accuracy and dynamic range has proven to be challenging. Here we employ a dual optical frequency comb spectrometer to readout a microfabricated cavity optomechanical accelerometer, allowing for rapid simultaneous measurements of the cavity&#39;

Istvan David, Dominik Bork, Gerti Kappel

The perception of the value and propriety of modern engineered systems is changing. In addition to their functional and extra-functional properties, nowadays&#39; systems are also evaluated by their sustainability properties. The next generation of systems will be characterized by an overall elevated sustainability -- including their post-life, driven by eff

Gurpreet S. Hora, Marco G. Giometto

Surrogate modeling is a viable solution for applications involving repetitive evaluations of expensive computational fluid dynamics models, such as uncertainty quantification and inverse problems. This study proposes a multi-layer perceptron (MLP) based machine-learning surrogate for canopy flow statistics accommodating any approaching mean-wind angle. The t

Anthony Hevia, Benjamin Kallus, Summer McClintic, Samantha Reisner

The edge clique cover (ECC) problem -- where the goal is to find a minimum cardinality set of cliques that cover all the edges of a graph -- is a classic NP-hard problem that has received much attention from both the theoretical and experimental algorithms communities. While small sparse graphs can be solved exactly via the branch-and-reduce algorithm of Gra

Lana Touma, Mohammad Al Horani, Manar Tailouni, Anas Dahabiah

Automatic Deception Detection has been a hot research topic for a long time, using machine learning and deep learning to automatically detect deception, brings new light to this old field. In this paper, we proposed a voting-based method for automatic deception detection from videos using audio, visual and lexical features. Experiments were done on two datas

Daniel Cariello

It was recently suggested that a solution to the separability problem for states that remain positive under partial transpose composed with realignment (the so-called symmetric with positive coefficients states or simply SPC states) could shed light on entanglement in general. Here we show that such a solution would solve the problem completely. Given a stat

George Lazarides, Rinku Maji, Qaisar Shafi

Composite topological structures such as superheavy &#34;quasi-stable strings&#34; (QSS) and &#34;walls bounded by strings&#34; (WBS) arise in realistic extensions of the Standard Model of high energy physics. We show that the gravitational radiation emitted in the early universe by these two unstable structures with a dimensionless string tension $Gμ\approx

Robert D. Gray, Mark Kambites

We continue the study of the structure of general subgroups (in particular maximal subgroups, also known as group $\mathcal{H}$-classes) of special inverse monoids. Recent research of the authors has established that these can be quite wild, but in this paper we show that if we restrict to special inverse monoids which are $E$-unitary (or have a weaker prope

Bence Hetényi, James R. Wootton

Spin qubits in semiconductor structures bring the promise of large-scale 2D integration, with the possibility to incorporate the control electronics on the same chip. In order to perform error correction on this platform, the characteristic features of spin qubits need to be accounted for. E.g., qubit readout involves an additional qubit which necessitates c

Alberto Hijano, Sakineh Vosoughi-nia, F. Sebastián Bergeret, Pauli Virtanen

We extend the Mattis-Bardeen theory for the dynamical response of superconductors to include different types of Hall responses. This is possible thanks to a recent modification of the quasiclassical Usadel equation, which allows for analyzing Hall effects in disordered superconductors and including the precise frequency dependence of such effects. Our result

Massimo Bertolini, Matteo Longo, Rodolfo Venerucci

The goal of this article is to obtain a proof of the Main conjectures of Iwasawa theory for rational elliptic curves over anticyclotomic extensions of imaginary quadratic fields, under mild arithmetic assumptions, both in the case where the rational prime $p$ is good ordinary or supersingular.

Namrata Vaswani

This note provides a significantly simpler and shorter proof of our sample complexity guarantee for solving the low rank column-wise sensing problem using the Alternating Gradient Descent (GD) and Minimization (AltGDmin) algorithm. AltGDmin was developed and analyzed for solving this problem in our recent work. We also provide an improved guarantee.

Apratim Bhattacharyya, Sunny Panchal, Mingu Lee, Reza Pourreza

Multi-modal language models (LM) have recently shown promising performance in high-level reasoning tasks on videos. However, existing methods still fall short in tasks like causal or compositional spatiotemporal reasoning over actions, in which model predictions need to be grounded in fine-grained low-level details, such as object motions and object interact

Michael Levet, Puck Rombach, Nicholas Sieger

In this paper, we show that computing canonical labelings of graphs of bounded rank-width is in $\textsf{TC}^{2}$. Our approach builds on the framework of Köbler & Verbitsky (CSR 2008), who established the analogous result for graphs of bounded treewidth. Here, we use the framework of Grohe & Neuen (ACM Trans. Comput. Log., 2023) to enumerate separators via

Luhuan Wu, Brian L. Trippe, Christian A. Naesseth, David M. Blei

Diffusion models have been successful on a range of conditional generation tasks including molecular design and text-to-image generation. However, these achievements have primarily depended on task-specific conditional training or error-prone heuristic approximations. Ideally, a conditional generation method should provide exact samples for a broad range of

R. Pablo Arribillaga, E. Pepa Risma

This paper explores many-to-one matching models, both with and without contracts, where doctors&#39; preferences are private and hospitals&#39; preferences are public and substitutable. It is known that any stable-dominating mechanism --which is either stable or individually rational and Pareto-dominates (from the doctors&#39; perspective) a stable mechanism

Maleeha Khawaja, Samir Siksek

A number field $K$ is primitive if $K$ and $\mathbb{Q}$ are the only subextensions of $K$. Let $C$ be a curve defined over $\mathbb{Q}$. We call an algebraic point $P\in C(\overline{\mathbb{Q}})$ primitive if the number field $\mathbb{Q}(P)$ is primitive. We present several sets of sufficient conditions for a curve $C$ to have finitely many primitive points